Data Mining Using Relational Database Management Systems

Software packages providing a whole set of data mining and machine learning algorithms are attractive because they allow experimentation with many kinds of algorithms in an easy setup. However, these packages are often based on main-memory data structures, limiting the amount of data they can handle. In this paper we use a relational database as secondary storage in order to eliminate this limitation. Unlike existing approaches, which often focus on optimizing a single algorithm to work with a database backend, we propose a general approach, which provides a database interface for several algorithms at once. We have taken a popular machine learning software package, Weka, and added a relational storage manager as back-tier to the system. The extension is transparent to the algorithms implemented in Weka, since it is hidden behind Weka’s standard main-memory data structure interface. Furthermore, some general mining tasks are transfered into the database system to speed up execution. We tested the extended system, refered to as WekaDB, and our results show that it achieves a much higher scalability than Weka, while providing the same output and maintaining good computation time

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that addresses these issues in isolation. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection/hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.Comment: 9 pages, 3 figure

GNUsmail: Open framework for on-line email classification

Real-time classification of massive email data is a challenging task that presents its own particular difficulties. Since email data presents an important temporal component, several problems arise: emails arrive continuously, and the criteria used to classify those emails can change, so the learning algorithms have to be able to deal with concept drift. Our problem is more general than spam detection, which has received much more attention in the literature. In this paper we present GNUsmail, an open-source extensible framework for email classification, which structure supports incremental and on-line learning. This framework enables the incorporation of algorithms developed by other researchers, such as those included in WEKA and MOA. We evaluate this framework, characterized by two overlapping phases (pre-processing and learning), using the ENRON dataset, and we compare the results achieved by WEKA and MOA algorithms

NCeSS Project : Data mining for social scientists

We will discuss the work being undertaken on the NCeSS data mining project, a one year project at the University of Manchester which began at the start of 2007, to develop data mining tools of value to the social science community. Our primary goal is to produce a suite of data mining codes, supported by a web interface, to allow social scientists to mine their datasets in a straightforward way and hence, gain new insights into their data. In order to fully define the requirements, we are looking at a range of typical datasets to find out what forms they take and the applications and algorithms that will be required. In this paper, we will describe a number of these datasets and will discuss how easily data mining techniques can be used to extract information from the data that would either not be possible or would be too time consuming by more standard methods

How to find a Morepork

In a previous blog (https://www.2040.co.nz/blogs/news/first-morepork-automatically-identified), you may have read about how I started down the road of automatically detecting morepork calls. Since then I’ve made some further progress and thought I’d share the journey so far with you